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本指南将帮助你开始使用 Perplexity chat models 。有关所有 ChatPerplexity 功能和配置的详细文档,请前往 API 参考 。
集成详情
Class Package Serializable PY support Downloads Version ChatPerplexity@langchain/communitybeta ✅
模型功能
请参阅下表标题中的链接,了解如何使用特定功能。
Note that at the time of writing, Perplexity only supports structured outputs on certain usage tiers.
要访问 Perplexity models,你需要create a Perplexity account, get an API key, and install the @langchain/community integration package.
前往 https://perplexity.ai 注册 Perplexity 并生成 API 密钥。完成后设置 PERPLEXITY_API_KEY 环境变量:
export PERPLEXITY_API_KEY = "your-api-key"
如果你想要自动追踪模型调用,还可以设置你的 LangSmith API 密钥,取消注释以下内容:
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"
LangChain 的 Perplexity 集成位于 @langchain/community 包中:
npm install @langchain/community @langchain/core
实例化
现在我们可以实例化模型对象并生成聊天补全:
import { ChatPerplexity } from "@langchain/community/chat_models/perplexity"
const llm = new ChatPerplexity ( {
model : "sonar" ,
temperature : 0 ,
maxTokens : undefined ,
timeout : undefined ,
maxRetries : 2 ,
// 其他参数...
} )
const aiMsg = await llm . invoke ([
{
role : "system" ,
content : "You are a helpful assistant that translates English to French. Translate the user sentence." ,
},
{
role : "user" ,
content : "I love programming." ,
},
])
aiMsg
AIMessage {
"id": "run-71853938-aa30-4861-9019-f12323c09f9a",
"content": "J'adore la programmation.",
"additional_kwargs": {
"citations": [
"https://careersatagoda.com/blog/why-we-love-programming/",
"https://henrikwarne.com/2012/06/02/why-i-love-coding/",
"https://forum.freecodecamp.org/t/i-love-programming-but/497502",
"https://ilovecoding.org",
"https://thecodinglove.com"
]
},
"response_metadata": {
"tokenUsage": {
"promptTokens": 20,
"completionTokens": 9,
"totalTokens": 29
}
},
"tool_calls": [],
"invalid_tool_calls": []
}
console . log (aiMsg . content)
J'adore la programmation.
API 参考
有关所有 ChatPerplexity 功能和配置的详细文档,请前往 API 参考 。
将这些文档连接到 Claude、VSCode 等工具,通过 MCP 获取实时答案。